In view of the issue above, we propose our 3D lane detection method, which has three contirbutions. Firstly, we introduce a data preprocessing module based on virtual camera, with transfroms the various cameras with different intrinsic and extrinsic parameters into a unified camera by homography matrix[1]}.Experiments show that this module has better allowed the network to extract the robust features of 2D images. Second, a pyramid module based on MLP spatial transformation is proposed. The above mentioned MLP spatial transformation performance is robust and easy to deploy. Inspired by ASPP[2]}, we also adde the concept of spatial feature pyramid. Experiments show that this method is very effective. Combined with the excellent work in target detection [3]}, [4]}, [5]}, different from the way of constructing anchors in [6]}, [7]}, [8]}, [9]}, we propose to add bin+offset to 3D lane detection.We find that these methods can better reduce the y-error and make the 3D lane detection more robust through experiments in OpenLane and Appolo dataset.
